High Cloud Cover Research Articles

On the Sensitivity of a Ground-Based Tropospheric Lidar to Aitken Mode Particles in the Upper Troposphere

Airborne observations have shown high concentrations of ultrafine aerosols in the Amazon upper troposphere (UT), which are key for replenishing the planetary boundary layer (PBL) with cloud condensation nuclei that sustain the “green ocean” clouds. Given their climatic relevance, long-term observations are needed, but aircraft measurements are only available in short-term campaigns. Alternatively, continuous observations of the aerosol vertical structure could be performed by a lidar (acronym for “light detection and ranging”) system in long-term campaigns. Here we assess whether a ground-based tropospheric lidar system could detect these ultrafine UT aerosols. To this aim, we simulated the lidar signal of a real instrument and then varied the instrument’s efficiency and the UT-particle concentration to determine under which conditions the detection is possible. Optical properties were computed with a Mie code based on the size distributions and numerical concentration profiles measured by the aircraft, and on the refractive indexes inverted from AERONET measurements. The aerosol optical depth (AOD) was retrieved by inverting the elastic lidar signal, and a statistical test was applied to evaluate the detection of the UT-aerosol layer. Our results indicate that, for the instrument we simulated, a 55-fold increase in the signal-to-noise ratio (SNR) is required for a 100% detection rate. This could be achieved by simultaneously time averaging over 30 min and spatially averaging to vertical bin lengths of 375 m, or by modifying the hardware. We repeated the analysis for under- and overestimated aerosol lidar ratio (Laer), and found that possible systematic errors did not affect the detection rate. Further studies are necessary to assess whether such long-time averages are feasible in the Amazon region (given the very high cloud cover), and to design a hardware upgrade. Although simulations and analyses here were based on a particular instrument and for the presence of new organic particles in the Amazonian upper troposphere, our methodology and results are general and applicable to other instruments and sites.

A simulation study on the climatic responses to short-lived climate pollutants changes from the pre-industrial era to the present

Short-lived climate pollutants (SLCPs) including methane, tropospheric ozone, and black carbon in this work, is a set of compounds with shorter lifetimes than carbon dioxide (CO2) and can cause warming effect on climate. Here, the effective radiative forcing (ERF) is estimated by using an online aerosol–climate model (BCC_AGCM2.0_CUACE/Aero); then the climate responses to SLCPs concentration changes from the pre-industrial era to the present (1850–2010) are estimated. The global annual mean ERF of SLCPs was estimated to be 0.99 [0.79–1.20] W m−2, and led to warming effects over most parts of the globe, with the warming center (about 1.0 K increase) being located in the mid-high latitudes of the Northern Hemisphere (NH) and the ocean around Antarctica. The changes in annual mean surface air temperature (SAT) caused by SLCPs changes were more prominent in the NH [0.78 (0.62–0.94) K] than in the Southern Hemisphere [0.62 (0.45–0.74) K], and the global annual mean value is 0.70 K. By looking at other variable responses, we found that precipitation had been increased by about 0.10 mm d−1 in mid- and high-latitudes and decreased by about 0.20 mm d−1 in subtropical regions, with the global annual mean value of 0.02 mm d−1. Changes in SLCPs also influenced atmospheric circulation change, a northward shift in the Intertropical Convergence Zone was induced due to the interhemispheric asymmetry in SAT. However, it is found in this work that SLCPs changes had little effect on global average cloud cover, whereas the local cloud cover changes could not be ignored, low cloud cover increase by about 2.5% over high latitudes in the NH and the ribbon area near 60°S, and high cloud cover increased by more than 2.0% over northern Africa and the Indian Ocean. Finally, we compared the ERFs and global and regional warming effects of SLCPs with those induced by CO2 changes. From 1850 to the present, the ERF of SLCPs was equivalent to 66%, 83%, and 50% of that of CO2 in global, NH, and SH mean, respectively. The increases in SAT caused by SLCPs were 43% and 55% of those by CO2 over the globe and China, respectively.

Vegetation Index Processing and Implications for Calculating Greenness in Population Health Studies

BACKGROUND: Surrounding vegetation (greenness) is an emerging exposure in population health studies. Yet, methods for processing satellite remote sensing data to calculate greenness are inconsistent across studies, which undermines comparisons of the extent of greenness health effects. AIM: We surveyed the literature for differences in calculating greenness indices and compared their summary values and classification into greenness quartiles (i.e., least to most green). METHODS: We identified three main differences in vegetation index processing: 1) averaging period (growing season vs. entire year), 2) exclusion of negative values, and 3) cloud masking (filtering out high cloud cover pixels). For each processing regime, we calculated the normalized vegetation difference index (NDVI) and enhanced vegetation index (EVI). We then assessed area-weighted averages at the Census tract level and compared greenness from the different methods in North Carolina. RESULTS: Differences in processing led to considerable differences in greenness summary values. NDVI and EVI were higher when the averaging period was restricted to the growing season instead of the entire year, or when negative values were omitted. In general, cloud masking led to higher NDVI or EVI. When categorizing North Carolina Census tracts into quartiles, cloud-masking reclassified as much as 70% of one quartile to another (i.e., third greenest to others). Comparing tracts’ quartiles averaged over the entire year to only the growing season, there was less reclassification with cloud masking. CONCLUSIONS: Methodological differences in data processing affect greenness exposure assessment (e.g., average NDVI / EVI, greenness quartiles). When comparing the magnitude and direction in health effects from greenness, such as in reviews or meta-analyses, researchers must account for methodological differences between each study’s handling of satellite remote sensing data. Future research should identify best practices (e.g., cloud masking) for calculating greenness in population health studies. KEYWORDS: Greenness, Vegetation, Exposure Misclassification, Nature, Remote Sensing, Methods

Cloud removal for optical remote sensing imagery using the SPA-CycleGAN network

Optical remote sensing images play an important role in many fields due to their natural visual representations. However, clouds obstruct a large proportion of recorded spectral signals and greatly affect the availability of high-quality optical images. In addition, the progress of cloud removal methods has long been restricted by the deficiency in theoretical basis and low computing power. With the advent of deep learning technology, this problem can be well addressed. We propose a network based on style transfer theory to remove thin and thick clouds in optical remote sensing images. An input data concatenation procedure with synthetic aperture radar (SAR) images is used to provide auxiliary information for the recovery of missing data caused by clouds. Moreover, the spatial attention mechanism adept at generating saliency information of image objects is also used to locate the clouds and corresponding shadows to make the network concentrate on the cloud region reconstruction task. Furthermore, to make the spatial attention mechanism self-adapted to the practical characteristics of clouded images, a loss function including a cloud mask is designed to assist the training step of the network. The proposed network is trained with a global open-source dataset and a region-specified dataset. The results of ablation experiments and comparison with baseline methods demonstrate that a high accuracy is guaranteed by the proposed model, even under high cloud coverage conditions [mean absolute error (MAE) = 0.0883, root-mean-square error (RMSE) = 0.1775, peak signal-to-noise ratio (PSNR) = 23.8, and structural similarity (SSIM) = 0.742). Using optical images independently to remove clouds results in poor results (MAE = 0.1156, RMSE = 0.2825, PSNR = 21.6, and SSIM = 0.596), which need to be addressed with data concatenation, such as SAR context information. Spatial attention mechanism plays an important role in improving the accuracy of cloud removal (MAE = 0.0251, RMSE = 0.0363, PSNR = 27.1, and SSIM = 0.876). For the missing values of optical images caused by cloud coverage, the Spatial-attention-CycleGAN proposed provides a potential solution for information recovery.

Evaluation of Chlorophyll-a estimation using Sentinel 3 based on various algorithms in southern coastal Vietnam

This paper aims to assess the potential of Ocean Land Colour Instrument (OLCI) for the retrieval of chlorophyll-a (chl-a) over southern coastal waters of Vietnam. For that purpose, four chlorophyll-a ocean color (OC) algorithms (OC4ME and three new OC version 7 OC4, OC5, OC6) were applied based on water-leaving reflectance obtained from two atmospheric correction processors (C2RCC and DSF). To overcome high cloud coverage in the area of interest, full spatial data reconstruction was implemented using Data Interpolating Empirical Orthogonal Functions (DINEOF). Numerical error metrics of in situ measurements (n = 49) collected in different ship-based campaigns has been assessed for Sentinel-3A (S-3A) and 3B (S-3B) as well as on the combined products built from these two later satellites. Results showed that products based on C2RRC significantly outperformed DSF. For chl-a algorithms, C2RCC-based OC5 gave the most accurate retrieval while applied to S-3A (R2: 0.58, RMSE: 1.018 mg m−3, MAPE: 49.4 %), S-3B (R2: 0.75, RMSE: 0.776 mg m−3, MAPE: 37.3 %), and synergy datasets (R2: 0.70, RMSE: 0.844 mg m−3, MAPE: 42.5 %). With>50 % of observations missing due to cloud cover, DINEOF provides a promising solution to reconstruct the full spatial information. The successfully demonstrated retrieval of chl-a in our study presents potential for daily monitoring when combining observations from S-3A/B to further improve our understanding of the spatio-temporal dynamics of coastal ecosystems.

Multidecadal Land Use Patterns and Land Surface Temperature Variation in Sri Lanka

Agricultural land conversion due to urbanization, industrialization, and many other factors is one of the significant concerns to food production. Therefore, analyzing the temporal and spatial variation of agricultural lands is an emerging topic in the research world. However, an agrarian country like Sri Lanka was given weaker attention to the temporal and spatial variation of the land use, including the agricultural lands. This study presents an extended analysis of temporal and spatial variation of land use patterns in Sri Lanka, specifically looking at the agricultural land conversion and land surface temperature (LST) change. Remote sensing techniques and geographic information system (GIS) were used for the presented work. The satellite images from three Landsat’s were analyzed for 2000, 2010, and 2020 to identify the potential land use conversions. In addition, LSTs were extracted for the same period. Significant and continuous increases can be seen in the agricultural lands from 33.94% (of total area) in 2000 to 43.2% in 2020. In contrast, the forest areas showcase a relative decrease from 38.51% to 33.82% (of total area) during the analyzed period. In addition, the rate of conversion from agriculture to settlements is higher in the latter decade (2010–2020) compared to the earlier decade (2000–2010). Only general conclusions were drafted based on the LSTs results as they were not extracted in the same months of the year due to high cloud cover. Therefore, the results and conclusions of this study can be effectively used to improve the land use policies in Sri Lanka and lead to a sustainable land use culture.

Development of High Resolution Cloud Cover Climatology Databank Using Merged Manual and Satellite Datasets over Ghana, West Africa

ABSTRACT Accurate and reliable total cloud cover (TCC) observation is essential for astronomy, renewable energy resource assessment, climate impact studies, and agriculture. In order to improve the spatial coverage for a climatological distribution pattern of TCC observation across different climatic zones in Ghana – West Africa, this paper developed a merged database comprising ground-based manual TCC observation dataset (TCCM) at 22 tropical synoptic stations and satellite-based TCC dataset retrieved from the NASA Prediction of Worldwide Energy Resource (POWER) climatological archives (TCCN) spanning 30 years (1983–2013) for each dataset. Firstly, the satellite data was assessed statistically for merging with station data. From the results, it is shown that on the overall, the satellite data (TCCN) is a good representation of local TCC climatology comparative to station observation by a mean percentage deviation of 7.8 ± 1.7, and indices of agreement between 0.7 and 0.99 ± 0.01, indicating strong zonal and seasonal similarities. Moreover, the best station-by-station similarities are over the northern half, being predominantly Savannah climate areas, while the southernmost half show the weakest similarities. This can be attributed to a complex interplay of coastal ocean-land-atmosphere interactions obstructing satellite sensing. Finally, the gridded merged dataset established that December–February is the lowest TCC season countrywide, whereas June–August is the highest TCC season, more pronounced over the southern half, being predominantly Forest climate type and showing significant non-linearity with atmospheric clarity indices. The results have useful applications for solar energy resource assessment, crop yield models, and provides a framework for development of cloud property and cloud radiative effect assessment for climate related studies.

Impact of Indian and East Asian Summer Monsoons on the Diurnal Temperature Range of the Low-Latitude Highlands of China in the Rainy Season

Abstract This study investigates the impact of the Indian and East Asian summer monsoons on the diurnal temperature range (DTR) in the low-latitude highlands of China (CLLH) based on in situ DTR observations, ERA5 data, and numerical simulations. Diagnoses indicate that the DTR in the CLLH shows a significant positive correlation with the Indian summer monsoon (ISM) and a negative correlation with the East Asian summer monsoon (EASM). When a strengthened ISM occurs with a weakened EASM, an anomalous anticyclonic circulation with downward motion is excited over the CLLH. This anomalous circulation pattern increases the DTR in the rainy season by reducing the medium and high cloud cover in the CLLH. A weakened ISM with a strengthened EASM decreases the DTR over the CLLH in the rainy season. Numerical experiments help to verify this crucial physical process linking the variability of the ISM and EASM with the DTR in the CLLH. The model results further indicate that the covariability of ISM and EASM contributes most to the variability of the rainy season DTR in the CLLH, followed by the individual variability of the EASM; the smallest contribution to the rainy season DTR in the CLLH is the individual variability of the ISM.

An NDVI Retrieval Method Based on a Double-Attention Recurrent Neural Network for Cloudy Regions

NDVI is an important parameter for environmental assessment and precision agriculture that well-describes the status of vegetation. Nevertheless, the clouds in optical images often result in the absence of NDVI information at key growth stages. The integration of SAR and optical image features will likely address this issue. Although the mapping of different data sources is complex, the prosperity of deep learning technology provides an alternative approach. In this study, the double-attention RNN architecture based on the recurrent neural network (RNN) and attention mechanism is proposed to retrieve NDVI data of cloudy regions. Overall, the NDVI is retrieved by the proposed model from two aspects: the temporal domain and the pixel neighbor domain. The performance of the double-attention RNN is validated through different cloud coverage conditions, input ablation, and comparative experiments with various methods. The results conclude that a high retrieval accuracy is guaranteed by the proposed model, even under high cloud coverage conditions (R2 = 0.856, RMSE = 0.124). Using SAR images independently results in poor NDVI retrieval results (R2 = 0.728, RMSE = 0.141) with considerable artifacts, which need to be addressed with auxiliary data, such as IDM features. Temporal and pixel neighbor features play an important role in improving the accuracy of NDVI retrieval (R2 = 0.894, RMSE = 0.096). For the missing values of NDVI data caused by cloud coverage, the double-attention RNN proposed in this study provides a potential solution for information recovery.

Detection of Forest Tree Losses in Côte d’Ivoire Using Drone Aerial Images

The fight against deforestation and forest degradation is now a major challenge for the preservation of global forest ecosystems. The remote sensing forest monitoring methods that are currently deployed are not always adapted to the Ivorian context because of the high cloud cover, diversity of shaded crops, and land clearing techniques. This study proposes a drone-based approach to assess intra-annual tree losses in the Bossématié classified forest. The method used is based on a detection analysis of tree losses in forest areas from a time series of aerial images acquired by drones from November 2018 to April 2019 on five sites in the studied forest. Based on photogrammetric models and photointerpretation, tree heights and tree crown sizes were estimated. Then, tree losses were detected based on the variation of tree heights during the study period. An analysis of the distribution of tree heights in Bossématié classified forest reveals that the maximum tree height was 65.06 m in November 2018 and 64.07 m in April 2019 with an average tree height of 34.29–37.00 m in November 2018 and 34.63–36.88 m in April 2019. The average tree crown area, meanwhile, was estimated to be 152 m². With an estimation accuracy of about 97%, these tree structural data indicate a minimum loss of 107 trees corresponding to a clearing area of 2 ha across all the surveyed sites from November 2018 to April 2019. This forest monitoring approach shows a considerable local loss of biodiversity and should be involved in the implementation of preservation, rehabilitation, and deployment strategies in an operational deforestation monitoring system in Côte d’Ivoire.

Satellite Observed Sensitivity of Tropical Clouds and Moisture to Sea Surface Temperature on Various Time and Space Scales: 1. Focus on High Level Cloud Situations Over Ocean

AbstractThis study examines variations in high cloud properties and relative humidity (RH) with sea surface temperature (SST) over tropical oceans (30°N–30°S) using spaceborne lidar and microwave radiometer observations. The mean values over the tropics indicate that middle‐tropospheric RH increases, high cloud covers decrease and cloud altitudes rise with SST. These signatures are consistent with the hypotheses proposed in the literature. The analysis of this same data set but at the scale of local processes shows different behaviors for SSTs <302 K and SSTs >302 K. Between 299 and 302 K, middle‐tropospheric RH, opaque cloud cover, and cloud top altitude increase together with SST, while optically thin cloud cover decreases. Over SSTs >302 K, middle‐tropospheric RH, opaque cloud cover, and opaque cloud top altitude decrease with SST, while the cover of optically thin clouds increases. Interestingly, the altitude of high clouds (not the cloud top) increases monotonically with SST from 299 to 305 K on a range of space and time scales, and the altitude of optically thin clouds remains higher than that of opaque clouds. The observed relationships on different time and space scales are compared to simulations of a global atmospheric model. Despite systematic biases, the model reproduces the sensitivity of the middle‐tropospheric RH and cloud altitude to SST rather well but fails to reproduce the variations of the balance between high opaque cloud cover and high optically thin cloud cover.

The Greater Mekong Subregion (GMS) and tropical expansion: A regional study of convection and precipitation

A series of data sets including brightness temperature from HIRS-UTWV, high cloud cover, precipitation and evaporation from ERA-Interim reanalysis data, and deep convective cloud from ISCCP were used to examine the latitudinal changes of the Hadley cell in the Greater Mekong Subregion (GMS) over the 1979–2018 period. In the study region, a pronounce poleward expansion was found during the winter season using three metrics of expansion: latitude of maximum brightness temperature associated with the anticyclonic center; latitude of zero crossing of precipitation minus evaporation, marking the extent of the tropical region; and latitude where high cloud cover reaches 50% of its maximum tropical value. However, no trends were discovered for the summer season. During the study period, there was an increase in equatorial convection as indicated by increased high cloud cover features. When precipitation minus evaporation was less than 0, there was diminishing dry zone evidence.

Modelling high-resolution Evapotranspiration in fragmented croplands from the constellation of Sentinels

The Sentinels for Evapotranspiration (Sen-ET) developed by the European Space Agency (ESA) provides a synoptic representation of actual ET (ETa) and other land-surface energy fluxes at high spatial (20 m) and temporal (daily) resolutions by the fusion of Sentinel-2 and Sentinel-3 datasets. However, the non-availability of optical sensor enabled Sentinel-2 images during the periods of high cloud cover such as monsoon limits the perennial use of Sen-ET. Here, we propose an alternate method to estimate ETa fluxes using locally observed weather data and the polarimetric parameters derived from Sentinel-1 with an application to fragmented, heterogeneous croplands. At first, the Sen-ET modelling approach with cloud-free datasets is validated using eddy covariance (EC) flux datasets (R2 = 0.74). Sen-ET derived ETa fluxes were used to develop crop coefficient (Kc) curves by considering reference ET (ET0) obtained from the weather data (Monteith, 1965b). Functional relations were then established between Sentinel-1 derived polarimetric parameters and Kc considering cloud-free datasets, and further translated to the periods of high cloud cover. We observed a strong dependency of VH on Kc for sugarcane and vegetables, and radar vegetation index (RVI) on Kc for rice (Rveg2=0.55,Rs.c.2=0.61,Rrice.2=0.51). The performance of Sentinel-1 derived ETa estimates were further validated with EC fluxes and found to be high during non-monsoon period (R2 = 0.74, RMSE = 1.25) than in monsoon period (R2 = 0.63, RMSE = 0.82). Our results indicate the suitability of Sentinel-1 in estimating ETa fluxes during the periods of high cloud cover for effective irrigation management.

Radar and optical remote sensing for near real-time assessments of cyclone impacts on coastal ecosystems.

Rapid impact assessment of cyclones on coastal ecosystems is critical for timely rescue and rehabilitation operations in highly human‐dominated landscapes. Such assessments should also include damage assessments of vegetation for restoration planning in impacted natural landscapes. Our objective is to develop a remote sensing‐based approach combining satellite data derived from optical (Sentinel‐2), radar (Sentinel‐1), and LiDAR (Global Ecosystem Dynamics Investigation) platforms for rapid assessment of post‐cyclone inundation in non‐forested areas and vegetation damage in a primarily forested ecosystem. We apply this multi‐scalar approach for assessing damages caused by the cyclone Amphan that hit coastal India and Bangladesh in May 2020, severely flooding several districts in the two countries, and causing destruction to the Sundarban mangrove forests. Our analysis shows that at least 6821 sq. km. land across the 39 study districts was inundated even after 10 days after the cyclone. We further calculated the change in forest greenness as the difference in normalized difference vegetation index (NDVI) pre‐ and post‐cyclone. Our findings indicate a <0.2 unit decline in NDVI in 3.45 sq. km. of the forest. Rapid assessment of post‐cyclone damage in mangroves is challenging due to limited navigability of waterways, but critical for planning of mitigation and recovery measures. We demonstrate the utility of Otsu method, an automated statistical approach of the Google Earth Engine platform to identify inundated areas within days after a cyclone. Our radar‐based inundation analysis advances current practices because it requires minimal user inputs, and is effective in the presence of high cloud cover. Such rapid assessment, when complemented with detailed information on species and vegetation composition, can inform appropriate restoration efforts in severely impacted regions and help decision makers efficiently manage resources for recovery and aid relief. We provide the datasets from this study on an open platform to aid in future research and planning endeavors.

Daily monitoring of marine mucilage using the MODIS products: a case study of 2021 mucilage bloom in the Sea of Marmara, Turkey.

Climate change and global warming along with human activities have caused abrupt changes in the atmosphere, marine, and terrestrial ecosystems. One of these changes is the rising number of mucilage events in marine ecosystems. During the recent two decades, mucilage blooms have begun to appear more frequently in the Sea of Marmara in Turkey, surrounded by seven densely populated cities with various anthropogenic activities including household pollution, heavy industrialization, agricultural pollution, commercial fishing, shipyards, and specialized marine terminals with high maritime traffic density. In Spring 2021, a massive mucilage event observed in the Sea of Marmara plagued the entire ecosystem and raised awareness among the government and the public to detect and monitor this phenomenon. In this research, daily monitoring and detection of mucilage formations from the coarse spatial resolution MODIS products were investigated during the 2021 bloom period. The results were validated with the reference mucilage datasets derived from Sentinel-2A imagery and in situ spectroradiometer measurements over mucilage formations. The results revealed that the MODIS surface reflectance profiles were highly correlated with the field spectral measurements and estimated mucilage formations were spatially overlapped with Sentinel-2A. Overall, the produced maps accurately depicted the mucilage-covered areas despite the limitations of unreliable estimates along the land-water transition lines, and no-data areas due to the low-quality observations and high cloud coverage.

Synergy of optical and synthetic aperture radar data for early-stage crop yield estimation: a case study over a state of Germany

Traditional crop cutting experiment-based yield estimation method captures the regional yield variability but lacks field-level information. Satellite images hold enormous crop information at finer spatial resolution. Crop yield mapping with optical images is particularly challenging if cloud-free images are unavailable during the crucial crop developmental stages. All-weather availability and sensitivity to crop structure, dielectric properties make synthetic aperture radar (SAR) images an excellent resource for yield estimation. Both types of data provide complementary information about crop conditions. A random forest regression model with genetic algorithm-based feature selection is developed to exploit the Sentinel-2 optical and Sentinel-1 SAR images for yield estimation. We utilized the crop harvest and quality survey (BEE) yield data set collected by the Hessisches Statistisches Landesamt (HSL), Wiesbaden, Germany, over 490 fields. We prepared 20 m resolution yield maps for winter wheat, winter barley, winter rye and winter rapeseed. Input features for the yield estimation model are selected based on the prior knowledge of remote sensing of vegetation. Baseline random forest regression models are developed for all the four crop types with optical and SAR input features. An optimized random forest regression model with genetic algorithm-based feature selection results in performance improvement. Dissimilarity in genetic algorithm selected image features highlights the significance of crop-specific feature selection for yield estimation. The optimized models reliably estimate yield by achieving correlation coefficient (r) of 0.65–0.86, mean absolute error 0.93–1.16 t ha–1 and root mean square error 1.12–1.56 t ha–1 with BEE yield on testing data set. The proposed models could estimate the intra-field yield variation when winter wheat, winter barley, winter rye were in the shooting phase to the beginning of ear-shifting, and winter rapeseed began to flower or was already flowering. These results demonstrate the merits of our model for early-stage crop yield estimation at the field level with mono-temporal image and adaptability for the cropping season with high cloud cover.

Assessment of Aeolian Activity in the Bodélé Depression, Chad: A Dense Spatiotemporal Time Series From Landsat-8 and Sentinel-2 Data

There are several hotspots of dust production in the central Sahara, the Bodélé Depression (BD) in northern Chad is considered the largest source of aerosol dust worldwide, with the fastest Barchan dunes that migrate southwesterly. Less is known about the complex patterns of dune movement in the BD, especially on a short time scale. Time-series inversion of optical image cross-correlation (TSI-OICC) proved to be a valuable method for monitoring historical movements with low uncertainties, high spatial coverage, and dense temporal coverage. We leveraged ∼8 years of Landsat-8 and ∼6 years of Sentinel-2 data to capture the dune migration patterns at BD. We used TSI-OICC, creating four independent networks of offset maps from Landsat-8 and Sentinel-2 images, and forming three networks by fusing data from the two sensors. We depended on the multi spatial coherence estimated from Sentinel-1 interferograms to automatically discriminate between the active and stagnant regions, which is important for the postprocessing steps. We combined the data from the two sensors in areas of overlap to assess the performance of the fusion between two sensors in increasing the temporal scale of the observations. Our results suggest that dune migration at BD is subject to seasonal and multiyear variations that differed spatially across the dune field. Seasonal variations were observed with migration slowing during the summer months. We estimated the median for velocities belonging to the same season and calculated the seasonal sliding coefficient (SSC) representing the ratio between seasonal velocities. The median SSC reached a maximum value of ∼2 for winter/summer, while the ratios were ∼1.10 and ∼1.35 for winter/spring and winter/autumn, respectively. The seasonal variability of the temporal patterns was strongly supported by the wind observations. Between (1984–1998 and 1998–2007) and (1998–2007 and 2013–2021), decelerations in dune velocities were observed with percentages of ∼4 and ∼28%, respectively, and these decelerations were supported by a deceleration in wind velocities. Inversion of time series provides dense spatiotemporal monitoring of the dune activity. The fusion between two sensors allows condensing the temporal sampling up to a weekly scale especially for locations exposed to contamination of high cloud cover or dust.

Suspended sediment transport estimation in Negro River (Amazon Basin) using MSI/Sentinel-2 data

The dynamics of suspended sediment production and transport in large rivers are essential geomorphologicalprocesses that can influence biodiversity. The aim of this work was to analyze the spatio-temporal variation of suspendedsediment transport in the lower Negro River using Sentinel-2 images. The suspended sediment concentration (SSC) wasestimated from the reflectance of band 4 of the MSI sensor onboard the Sentinel-2A and Sentinel-2B. The results indicate thatthe hydrological regime controls the temporal variability of SSC, being higher in the low water period and lower duringflooding, with a mean concentration of 5.28 mg L-1 for the period 2015–2019. Analysis of the spatial variation of SSC inSentinel-2 images indicated a zone of active sediment deposition located downstream of the islands of the AnavilhanasArchipelago. Geology, forest cover, and hydrological variability are the main factors controlling the low sediment yield of thebasin, with an estimated suspended solid flux of 5.76×106 ton∙year-1 and a specific yield of 8 ton∙km-2∙year-1. Despite the lowerquantity of images between February and April, due to high cloud cover, this study showed that Sentinel-2 images could beused to monitor the temporal and spatial dynamics of suspended sediment transport in Amazonian blackwater rivers.

Utilization of Synthetic Aperture Radar (SAR) Imagery to Identify Potential for Earthquake Disasters to Improve Disaster Preparedness

Indonesia is located in three collision zones of the world’s three major plates, namely the Eurasian Plate, the Indo-Australian Plate, and the Pacific Plate. As a result of the plate collision, there were many earthquakes in Indonesia. One of the characteristics of the earthquake in this study is an active fault. Active faults are things that can cause earthquakes. The purpose of this literature review is to determine the differences in the accuracy of the Synthetic Aperture Radar (SAR) method with various types of satellites to identify earthquake characteristics. This literature review is limited by analysis of active faults on land using SAR imagery in the territory of Indonesia. Indonesia is located in the equator causes high cloud coverage throughout the year in Indonesian region. The advantages of this SAR image are that it can monitor the earth’s surface without weather disturbances, can penetrate clouds, and can be used during the day or night. The results of this literature review show that to identify the impact of earthquakes with high slip fault sources, medium resolution SAR images can be used (such as using Sentinel-1 imagery), and to identify the effect of earthquakes with low slip faults, high resolution can be used (such as using ALOS-2/PALSAR imagery). Knowing the impact of deformation due to earthquakes can be used to improve disaster preparedness, particularly those caused by earthquakes.

Interference between Total Solar Radiation and Cloud Cover over Baghdad City

Clouds greatly affect the elements of climate, energy balance, and solar radiation, which has increased the interest of many researchers in trying to find the best relationships and formulas that link these variables. In this work, the data of the European Center for Medium-Range Weather Forecasts (ECMWF) was relied on. The research aims to find the overlap between Cloud Cover (Low, Middle, High, and Total), (LCC, MCC, HCC, and TCC) respectively, with Total Solar Radiation (TSR) of Baghdad city, for the period (1981-2013), the work was carried out with the monthly and annual data of the (Low, Middle, High, and Total) cloud cover and the total solar radiation falling on a horizontal surface. And by using the correlation coefficient Spearman rho test (rs) to find the strength of the relationship between total solar radiation and cloud cover, it was found that there is an inverse relationship between the total solar radiation falling on a horizontal surface and the (Low, Middle, High, and Total) cloud cover.